Clustering-based Creator Correlation Analysis of Little Red Book
Wenxuan Chen · Media and Communication Research · 2024
The proliferation of social commerce platforms, exemplified by Little Red Book, has created unprecedented opportunities for analyzing consumer behavior through user-generated content. This paper introduces a pioneering clustering analysis model tailored to the distinct environment of Little Red Book. Our model integrates advanced data mining techniques with natural language processing to systematically categorize user posts, reviews, and interactions, aiming to decipher complex consumer behavior patterns. By employing this model, we are able to identify distinct consumer clusters based on preferences, engagement levels, and sentiment towards products and brands. This segmentation enables a deeper understanding of market trends, user needs, and potential areas for product innovation. Our methodology is validated through a series of experiments, demonstrating the model's effectiveness in providing actionable insights for marketers, product developers, and platform managers. The findings underscore the potential of targeted clustering analysis in enhancing strategic decision-making in the digital marketplace. This study not only contributes a novel tool for academic and practical applications within the realm of social commerce analytics but also sets a foundation for future research in consumer behavior analysis on digital platforms.